As　a　powerful　optimization　technique,　multi-objective　particle　swarm　optimization　algorithms　have　been　widely　used　in　various　fields.　However,　performing　well　in　terms　of　convergence　and　diversity　simultaneously　is　still　a　challenging　task　for　most　existing　algorithms.　In　this　paper,　a　multi-objective　particle　swarm　optimization　algorithm　based　on　two-archive　mechanism　(MOPSO_TA)　is　proposed　for　the　above　challenge.　First,　two　archives,　including　convergence　archive　(CA)　and　diversity　archive　(DA)　are　designed　to　emphasize　convergence　and　diversity　separately.　On　one　hand,　particles　are　updated　by　indicator-based　scheme　to　provide　selection　pressure　toward　the　optimal　direction　in　CA.　On　the　other　hand,　shift-based　density　estimation　and　similarity　measure　are　adopted　to　preserve　diverse　candidate　solutions　in　DA.　Second,　the　genetic　operators　are　conducted　on　particles　from　CA　and　DA　to　further　enhance　the　quality　of　solutions　as　global　leaders.　Then　the　search　ability　of　MOPSO_TA　can　be　improved　by　performing　hybrid　operators.　Furthermore,　to　balance　global　exploration　and　local　exploitation　of　MOPSO_TA,　a　flight　parameters　adjustment　mechanism　is　developed　based　on　the　evolutionary　information.　Finally,　the　proposed　algorithm　is　compared　experimentally　with　several　representative　multi-objective　optimization　algorithms　on　21　benchmark　functions.　The　experimental　results　demonstrate　the　competitiveness　and　effectiveness　of　the　proposed　method.(c)　2022　Elsevier　B.V.　All　rights　reserved.